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Author:

Liu, Yinyang (Liu, Yinyang.) | Xu, Xiaobin (Xu, Xiaobin.) | Li, Feixiang (Li, Feixiang.)

Indexed by:

EI Scopus

Abstract:

Deep learning have achieved great success both in image and natural language processing. When to search similar images, there are some data occur which image and image title are not related. To deal with this problem which involves the process of both image and natural language, we propose a convolutional neural network model. The model both uses the feature of images and texts to judge the similarity. In the model, the two type of feature extracted respectively and then give the probability of the relationship between images and titles. This probability is added to the search strategy as a score to improve search quality. © 2018 IEEE.

Keyword:

Convolutional neural networks Image processing Deep learning Natural language processing systems Classification (of information) Software engineering

Author Community:

  • [ 1 ] [Liu, Yinyang]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
  • [ 2 ] [Xu, Xiaobin]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
  • [ 3 ] [Li, Feixiang]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China

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ISSN: 2327-0586

Year: 2018

Volume: 2018-November

Page: 844-847

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 7

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